MasterSet: A Large-Scale Benchmark for Must-Cite Citation Recommendation in the AI/ML Literature

• URL: http://arxiv.org/abs/2604.17680v1
• Date: Mon, 20 Apr 2026 00:34:28 GMT
• Title: MasterSet: A Large-Scale Benchmark for Must-Cite Citation Recommendation in the AI/ML Literature
• Abstract summary: MasterSet is a large-scale benchmark designed to evaluate must-cite recommendation in the AI/ML domain.<n>It incorporates over 150,000 papers collected from official conference proceedings/websites of 15 leading venues.<n>The benchmark task requires retrieving must-cite papers from the candidate pool given only a query paper's title and abstract.
• Score: 14.082854604879572
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The explosive growth of AI and machine learning literature -- with venues like NeurIPS and ICLR now accepting thousands of papers annually -- has made comprehensive citation coverage increasingly difficult for researchers. While citation recommendation has been studied for over a decade, existing systems primarily focus on broad relevance rather than identifying the critical set of ``must-cite'' papers: direct experimental baselines, foundational methods, and core dependencies whose omission would misrepresent a contribution's novelty or undermine reproducibility. We introduce MasterSet, a large-scale benchmark specifically designed to evaluate must-cite recommendation in the AI/ML domain. MasterSet incorporates over 150,000 papers collected from official conference proceedings/websites of 15 leading venues, serving as a comprehensive candidate pool for retrieval. We annotate citations with a three-tier labeling scheme: (I) experimental baseline status, (II) core relevance (1--5 scale), and (III) intra-paper mention frequency. Our annotation pipeline leverages an LLM-based judge, validated by human experts on a stratified sample. The benchmark task requires retrieving must-cite papers from the candidate pool given only a query paper's title and abstract, evaluated by Recall@$K$. We establish baselines using sparse retrieval, dense scientific embeddings, and graph-based methods, demonstrating that must-cite retrieval remains a challenging open problem.

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